mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-09-11 12:24:53 +00:00
add speculative head
This commit is contained in:
OlivierDehaene
parent
d1d757e676
commit
725f0e350d
@ -5,80 +5,80 @@
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@ -5,80 +5,80 @@
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@ -6,82 +6,82 @@
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"special": false
|
||||
"id": 2,
|
||||
"logprob": -2.9140625,
|
||||
"special": false,
|
||||
"text": "#"
|
||||
},
|
||||
{
|
||||
"id": 2701,
|
||||
"text": " following",
|
||||
"logprob": -3.6445312,
|
||||
"special": false
|
||||
"id": 4230,
|
||||
"logprob": -3.1054688,
|
||||
"special": false,
|
||||
"text": " Create"
|
||||
},
|
||||
{
|
||||
"id": 729,
|
||||
"text": " function",
|
||||
"logprob": -2.9648438,
|
||||
"special": false
|
||||
"id": 264,
|
||||
"logprob": -1.0966797,
|
||||
"special": false,
|
||||
"text": " a"
|
||||
},
|
||||
{
|
||||
"id": 271,
|
||||
"text": "\n\n",
|
||||
"logprob": -1.9111328,
|
||||
"special": false
|
||||
"id": 1681,
|
||||
"logprob": -1.6914062,
|
||||
"special": false,
|
||||
"text": " request"
|
||||
},
|
||||
{
|
||||
"id": 31946,
|
||||
"text": "Inputs",
|
||||
"logprob": -1.6855469,
|
||||
"special": false
|
||||
"id": 198,
|
||||
"logprob": -1.1923828,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 25,
|
||||
"text": ":",
|
||||
"logprob": -1.6093254e-05,
|
||||
"special": false
|
||||
"id": 2035,
|
||||
"logprob": -1.3193359,
|
||||
"special": false,
|
||||
"text": "request"
|
||||
},
|
||||
{
|
||||
"id": 707,
|
||||
"text": " def",
|
||||
"logprob": -0.5678711,
|
||||
"special": false
|
||||
"id": 284,
|
||||
"logprob": -0.13586426,
|
||||
"special": false,
|
||||
"text": " ="
|
||||
},
|
||||
{
|
||||
"id": 1477,
|
||||
"text": " find",
|
||||
"logprob": -2.5917969,
|
||||
"special": false
|
||||
"id": 7388,
|
||||
"logprob": -1.2412109,
|
||||
"special": false,
|
||||
"text": " requests"
|
||||
},
|
||||
{
|
||||
"id": 6345,
|
||||
"text": "_max",
|
||||
"logprob": -1.8349609,
|
||||
"special": false
|
||||
"id": 670,
|
||||
"logprob": -0.2775879,
|
||||
"special": false,
|
||||
"text": ".get"
|
||||
}
|
||||
],
|
||||
"top_tokens": null
|
||||
},
|
||||
"generated_text": " for the following function\n\nInputs: def find_max"
|
||||
"generated_text": "\n# Create a request\nrequest = requests.get"
|
||||
},
|
||||
{
|
||||
"details": {
|
||||
@ -258,81 +258,81 @@
|
||||
"generated_tokens": 10,
|
||||
"prefill": [
|
||||
{
|
||||
"id": 2271,
|
||||
"text": "Test",
|
||||
"logprob": null
|
||||
"id": 2271,
|
||||
"logprob": null,
|
||||
"text": "Test"
|
||||
},
|
||||
{
|
||||
"id": 1681,
|
||||
"text": " request",
|
||||
"logprob": -7.0351562
|
||||
"id": 1681,
|
||||
"logprob": -8.8515625,
|
||||
"text": " request"
|
||||
}
|
||||
],
|
||||
"seed": null,
|
||||
"tokens": [
|
||||
{
|
||||
"id": 369,
|
||||
"text": " for",
|
||||
"logprob": -2.1914062,
|
||||
"special": false
|
||||
"id": 198,
|
||||
"logprob": -2.9023438,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 279,
|
||||
"text": " the",
|
||||
"logprob": -2.6210938,
|
||||
"special": false
|
||||
"id": 2,
|
||||
"logprob": -2.9140625,
|
||||
"special": false,
|
||||
"text": "#"
|
||||
},
|
||||
{
|
||||
"id": 2701,
|
||||
"text": " following",
|
||||
"logprob": -3.6445312,
|
||||
"special": false
|
||||
"id": 4230,
|
||||
"logprob": -3.1054688,
|
||||
"special": false,
|
||||
"text": " Create"
|
||||
},
|
||||
{
|
||||
"id": 729,
|
||||
"text": " function",
|
||||
"logprob": -2.9648438,
|
||||
"special": false
|
||||
"id": 264,
|
||||
"logprob": -1.0966797,
|
||||
"special": false,
|
||||
"text": " a"
|
||||
},
|
||||
{
|
||||
"id": 271,
|
||||
"text": "\n\n",
|
||||
"logprob": -1.9111328,
|
||||
"special": false
|
||||
"id": 1681,
|
||||
"logprob": -1.6914062,
|
||||
"special": false,
|
||||
"text": " request"
|
||||
},
|
||||
{
|
||||
"id": 31946,
|
||||
"text": "Inputs",
|
||||
"logprob": -1.6855469,
|
||||
"special": false
|
||||
"id": 198,
|
||||
"logprob": -1.1923828,
|
||||
"special": false,
|
||||
"text": "\n"
|
||||
},
|
||||
{
|
||||
"id": 25,
|
||||
"text": ":",
|
||||
"logprob": -1.6093254e-05,
|
||||
"special": false
|
||||
"id": 2035,
|
||||
"logprob": -1.3193359,
|
||||
"special": false,
|
||||
"text": "request"
|
||||
},
|
||||
{
|
||||
"id": 707,
|
||||
"text": " def",
|
||||
"logprob": -0.5678711,
|
||||
"special": false
|
||||
"id": 284,
|
||||
"logprob": -0.13586426,
|
||||
"special": false,
|
||||
"text": " ="
|
||||
},
|
||||
{
|
||||
"id": 1477,
|
||||
"text": " find",
|
||||
"logprob": -2.5917969,
|
||||
"special": false
|
||||
"id": 7388,
|
||||
"logprob": -1.2412109,
|
||||
"special": false,
|
||||
"text": " requests"
|
||||
},
|
||||
{
|
||||
"id": 6345,
|
||||
"text": "_max",
|
||||
"logprob": -1.8349609,
|
||||
"special": false
|
||||
"id": 670,
|
||||
"logprob": -0.2775879,
|
||||
"special": false,
|
||||
"text": ".get"
|
||||
}
|
||||
],
|
||||
"top_tokens": null
|
||||
},
|
||||
"generated_text": " for the following function\n\nInputs: def find_max"
|
||||
"generated_text": "\n# Create a request\nrequest = requests.get"
|
||||
}
|
||||
]
|
||||
|
||||
@ -3,7 +3,7 @@ import pytest
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def flash_qwen2_handle(launcher):
|
||||
with launcher("Qwen/Qwen1.5-7B") as handle:
|
||||
with launcher("Qwen/Qwen1.5-0.5B") as handle:
|
||||
yield handle
|
||||
|
||||
|
||||
@ -20,7 +20,7 @@ async def test_flash_qwen2(flash_qwen2, response_snapshot):
|
||||
)
|
||||
|
||||
assert response.details.generated_tokens == 10
|
||||
assert response.generated_text == " for the following function\n\nInputs: def find_max"
|
||||
assert response.generated_text == "\n# Create a request\nrequest = requests.get"
|
||||
assert response == response_snapshot
|
||||
|
||||
|
||||
@ -48,14 +48,12 @@ async def test_flash_qwen2_all_params(flash_qwen2, response_snapshot):
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_flash_qwen2_load(flash_qwen2, generate_load, response_snapshot):
|
||||
responses = await generate_load(
|
||||
flash_qwen2, "Test request", max_new_tokens=10, n=4
|
||||
)
|
||||
responses = await generate_load(flash_qwen2, "Test request", max_new_tokens=10, n=4)
|
||||
|
||||
assert len(responses) == 4
|
||||
assert all(
|
||||
[r.generated_text == responses[0].generated_text for r in responses]
|
||||
), f"{[r.generated_text for r in responses]}"
|
||||
assert responses[0].generated_text == ": Let n = 10 - 1"
|
||||
assert responses[0].generated_text == "\n# Create a request\nrequest = requests.get"
|
||||
|
||||
assert responses == response_snapshot
|
||||
|
||||
@ -332,27 +332,6 @@ def get_model(
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
elif model_type == "qwen2":
|
||||
if FLASH_ATTENTION:
|
||||
return FlashQwen2(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
elif sharded:
|
||||
raise NotImplementedError(
|
||||
FLASH_ATT_ERROR_MESSAGE.format("Sharded Qwen2")
|
||||
)
|
||||
else:
|
||||
return CausalLM(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
if model_type == "gemma":
|
||||
if FLASH_ATTENTION:
|
||||
return FlashGemma(
|
||||
@ -364,9 +343,7 @@ def get_model(
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
elif sharded:
|
||||
raise NotImplementedError(
|
||||
FLASH_ATT_ERROR_MESSAGE.format("Sharded Golden Gate")
|
||||
)
|
||||
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Gemma"))
|
||||
else:
|
||||
return CausalLM(
|
||||
model_id,
|
||||
@ -424,6 +401,17 @@ def get_model(
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
elif sharded:
|
||||
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Mistral"))
|
||||
else:
|
||||
return CausalLM(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
use_medusa=use_medusa,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
if model_type == "mixtral":
|
||||
sliding_window = config_dict.get("sliding_window", -1)
|
||||
@ -438,6 +426,18 @@ def get_model(
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
elif sharded:
|
||||
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Mixtral"))
|
||||
else:
|
||||
return CausalLM(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
use_medusa=use_medusa,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
if model_type == "starcoder2":
|
||||
sliding_window = config_dict.get("sliding_window", -1)
|
||||
if (
|
||||
@ -450,6 +450,43 @@ def get_model(
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
elif sharded:
|
||||
raise NotImplementedError(
|
||||
FLASH_ATT_ERROR_MESSAGE.format("Sharded Starcoder2")
|
||||
)
|
||||
else:
|
||||
return CausalLM(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
use_medusa=use_medusa,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
if model_type == "qwen2":
|
||||
sliding_window = config_dict.get("sliding_window", -1)
|
||||
if (
|
||||
(sliding_window is None or sliding_window == -1) and FLASH_ATTENTION
|
||||
) or HAS_FLASH_ATTN_V2_CUDA:
|
||||
return FlashQwen2(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
elif sharded:
|
||||
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Qwen2"))
|
||||
else:
|
||||
return CausalLM(
|
||||
model_id,
|
||||
revision,
|
||||
quantize=quantize,
|
||||
use_medusa=use_medusa,
|
||||
dtype=dtype,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
if model_type == "opt":
|
||||
return OPTSharded(
|
||||
|
||||
@ -486,6 +486,9 @@ class CausalLM(Model):
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
if use_medusa:
|
||||
raise RuntimeError("Medusa decoding is not enabled for AutoModel")
|
||||
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda")
|
||||
dtype = torch.float16 if dtype is None else dtype
|
||||
|
||||
@ -870,7 +870,7 @@ class BloomForCausalLM(BloomPreTrainedModel):
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
**deprecated_arguments,
|
||||
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
|
||||
) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
|
||||
r"""
|
||||
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||||
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
||||
|
||||
@ -11,7 +11,7 @@ from text_generation_server.utils.layers import (
|
||||
TensorParallelColumnLinear,
|
||||
TensorParallelEmbedding,
|
||||
PositionRotaryEmbedding,
|
||||
TensorParallelHead,
|
||||
SpeculativeHead,
|
||||
get_linear,
|
||||
FastRMSNorm,
|
||||
)
|
||||
@ -51,8 +51,14 @@ def _load_gqa(config, prefix: str, weights):
|
||||
config.hidden_size,
|
||||
], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
|
||||
|
||||
w = [
|
||||
weights.get_sharded(f"{p}.bias", dim=0)
|
||||
for p in [f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"]
|
||||
]
|
||||
bias = torch.cat(w, dim=0).to(dtype=weights.dtype).to(device=weights.device)
|
||||
|
||||
return TensorParallelColumnLinear(
|
||||
get_linear(weight, bias=None, quantize=config.quantize)
|
||||
get_linear(weight, bias=bias, quantize=config.quantize)
|
||||
)
|
||||
|
||||
|
||||
@ -170,6 +176,7 @@ class Qwen2Attention(torch.nn.Module):
|
||||
|
||||
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
|
||||
|
||||
|
||||
class Qwen2MLP(nn.Module):
|
||||
def __init__(self, prefix, config, weights):
|
||||
super().__init__()
|
||||
@ -212,7 +219,9 @@ class Qwen2Layer(nn.Module):
|
||||
def __init__(self, layer_id, config, weights):
|
||||
super().__init__()
|
||||
prefix = f"model.layers.{layer_id}"
|
||||
self.self_attn = Qwen2Attention(prefix=f"{prefix}.self_attn", config=config, weights=weights)
|
||||
self.self_attn = Qwen2Attention(
|
||||
prefix=f"{prefix}.self_attn", config=config, weights=weights
|
||||
)
|
||||
self.mlp = Qwen2MLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
|
||||
self.input_layernorm = FastRMSNorm.load(
|
||||
prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
|
||||
@ -262,6 +271,7 @@ class Qwen2Layer(nn.Module):
|
||||
|
||||
return mlp_output, attn_res
|
||||
|
||||
|
||||
class Qwen2Model(torch.nn.Module):
|
||||
def __init__(self, config, weights):
|
||||
super().__init__()
|
||||
@ -338,7 +348,7 @@ class Qwen2ForCausalLM(torch.nn.Module):
|
||||
super().__init__()
|
||||
|
||||
self.model = Qwen2Model(config, weights)
|
||||
self.lm_head = TensorParallelHead.load(
|
||||
self.lm_head = SpeculativeHead.load(
|
||||
config,
|
||||
prefix="lm_head",
|
||||
weights=weights,
|
||||
|
||||
@ -721,7 +721,7 @@ class GPTNeoxForCausalLM(GPTNeoXPreTrainedModel):
|
||||
)
|
||||
|
||||
hidden_states = outputs[0]
|
||||
lm_logits = self.embed_out(hidden_states)
|
||||
lm_logits, speculative_logits = self.embed_out(hidden_states)
|
||||
|
||||
lm_loss = None
|
||||
if labels is not None:
|
||||
@ -739,12 +739,15 @@ class GPTNeoxForCausalLM(GPTNeoXPreTrainedModel):
|
||||
output = (lm_logits,) + outputs[1:]
|
||||
return ((lm_loss,) + output) if lm_loss is not None else output
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=lm_loss,
|
||||
logits=lm_logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
return (
|
||||
CausalLMOutputWithPast(
|
||||
loss=lm_loss,
|
||||
logits=lm_logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
),
|
||||
speculative_logits,
|
||||
)
|
||||
|
||||
def prepare_inputs_for_generation(
|
||||
|
||||
@ -792,16 +792,19 @@ class OPTForCausalLM(OPTPreTrainedModel):
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
logits = self.lm_head(outputs[0]).contiguous()
|
||||
logits, speculative_logits = self.lm_head(outputs)
|
||||
|
||||
loss = None
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
return (
|
||||
CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
),
|
||||
speculative_logits,
|
||||
)
|
||||
|
||||
def prepare_inputs_for_generation(
|
||||
|
||||
@ -315,7 +315,7 @@ class BaseFlashMistral(FlashCausalLM):
|
||||
device = torch.device(f"cuda:{rank}")
|
||||
dtype = torch.float16 if dtype is None else dtype
|
||||
else:
|
||||
raise NotImplementedError("FlashLlama is only available on GPU")
|
||||
raise NotImplementedError("FlashMistral is only available on GPU")
|
||||
|
||||
tokenizer = LlamaTokenizerFast.from_pretrained(
|
||||
model_id,
|
||||
|
||||
@ -1,12 +1,17 @@
|
||||
import math
|
||||
|
||||
import torch
|
||||
import torch.distributed
|
||||
|
||||
from opentelemetry import trace
|
||||
from transformers import AutoTokenizer
|
||||
from transformers.models.qwen2 import Qwen2Tokenizer
|
||||
from typing import Optional
|
||||
|
||||
from text_generation_server.models import FlashCausalLM
|
||||
from text_generation_server.models.cache_manager import BLOCK_SIZE
|
||||
from text_generation_server.models.flash_mistral import (
|
||||
BaseFlashMistral,
|
||||
set_sliding_window,
|
||||
)
|
||||
from text_generation_server.models.custom_modeling.flash_qwen2_modeling import (
|
||||
Qwen2ForCausalLM,
|
||||
)
|
||||
@ -20,12 +25,13 @@ from text_generation_server.utils import (
|
||||
tracer = trace.get_tracer(__name__)
|
||||
|
||||
|
||||
class FlashQwen2(FlashCausalLM):
|
||||
class FlashQwen2(BaseFlashMistral):
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
use_medusa: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
@ -36,23 +42,25 @@ class FlashQwen2(FlashCausalLM):
|
||||
else:
|
||||
raise NotImplementedError("FlashQwen2 is only available on GPU")
|
||||
|
||||
try:
|
||||
tokenizer = Qwen2Tokenizer.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
except Exception:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
tokenizer = Qwen2Tokenizer.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
|
||||
config = Qwen2Config.from_pretrained(
|
||||
model_id, revision=revision, trust_remote_code=trust_remote_code
|
||||
)
|
||||
config.quantize = quantize
|
||||
config.use_medusa = use_medusa
|
||||
|
||||
# Set context windows
|
||||
if config.sliding_window is not None:
|
||||
set_sliding_window(
|
||||
config.sliding_window, math.ceil(config.sliding_window / BLOCK_SIZE)
|
||||
)
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
|
||||
@ -63,8 +71,10 @@ class FlashQwen2(FlashCausalLM):
|
||||
|
||||
model = Qwen2ForCausalLM(config, weights)
|
||||
|
||||
self.cuda_graphs = {}
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
super(FlashQwen2, self).__init__(
|
||||
super(BaseFlashMistral, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
num_layers=len(model.model.layers),
|
||||
@ -74,4 +84,5 @@ class FlashQwen2(FlashCausalLM):
|
||||
device=device,
|
||||
rank=rank,
|
||||
world_size=world_size,
|
||||
sliding_window=config.sliding_window,
|
||||
)
|
||||
|
||||
@ -38,7 +38,7 @@ class FlashStarcoder2(BaseFlashMistral):
|
||||
device = torch.device(f"cuda:{rank}")
|
||||
dtype = torch.float16 if dtype is None else dtype
|
||||
else:
|
||||
raise NotImplementedError("FlashLlama is only available on GPU")
|
||||
raise NotImplementedError("FlashStarcoder2 is only available on GPU")
|
||||
|
||||
tokenizer = GPT2TokenizerFast.from_pretrained(
|
||||
model_id,
|
||||
|
||||
@ -167,6 +167,7 @@ class GalacticaSharded(CausalLM):
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
use_medusa: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
@ -194,6 +195,7 @@ class GalacticaSharded(CausalLM):
|
||||
)
|
||||
config.quantize = quantize
|
||||
tokenizer.pad_token_id = config.pad_token_id
|
||||
config.use_medusa = use_medusa
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
|
||||
@ -229,10 +231,10 @@ class GalacticaSharded(CausalLM):
|
||||
def forward(
|
||||
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
|
||||
):
|
||||
outputs = self.model.forward(
|
||||
outputs, speculative_logits = self.model.forward(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
use_cache=True,
|
||||
)
|
||||
return outputs.logits, outputs.past_key_values
|
||||
return outputs.logits, speculative_logits, outputs.past_key_values
|
||||
|
||||
@ -24,6 +24,7 @@ class GPTNeoxSharded(CausalLM):
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
use_medusa: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
@ -50,6 +51,7 @@ class GPTNeoxSharded(CausalLM):
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
config.quantize = quantize
|
||||
config.use_medusa = use_medusa
|
||||
|
||||
torch.distributed.barrier(group=self.process_group)
|
||||
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
|
||||
@ -75,7 +77,7 @@ class GPTNeoxSharded(CausalLM):
|
||||
def forward(
|
||||
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
|
||||
):
|
||||
outputs = self.model.forward(
|
||||
outputs, speculative_logits = self.model.forward(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
@ -84,4 +86,4 @@ class GPTNeoxSharded(CausalLM):
|
||||
)
|
||||
|
||||
logits = outputs.logits
|
||||
return logits, outputs.past_key_values
|
||||
return logits, speculative_logits, outputs.past_key_values
|
||||
|
||||
@ -12,9 +12,13 @@ class RW(CausalLM):
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
use_medusa: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
if use_medusa:
|
||||
raise RuntimeError("Medusa decoding is not enabled for AutoModel")
|
||||
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda")
|
||||
dtype = torch.float16 if dtype is None else dtype
|
||||
|
||||
@ -536,6 +536,9 @@ class Seq2SeqLM(Model):
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
if use_medusa:
|
||||
raise RuntimeError("Medusa decoding is not enabled for AutoModel")
|
||||
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda")
|
||||
dtype = torch.float16 if dtype is None else dtype
|
||||
|
||||
Loading…
Reference in New Issue
Block a user